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However, this model predicts a strong correlation between item popularity and item age that is not observed in the WWW [24], nor in the citation network [25].
For each item, age of first occurrence, frequency of occurrence, as well as most common perpetrator is asked.
Linear regression models were used to predict valuations from 11 of the 12 items on the SF-12 (excluding the global health item), age, and gender.
It is possible to assess the adequacy of the one factor solution by observing that loadings of most items are above 0.6, with only the first item (Age category) having a loading of 0.375; and taking into consideration the high value of the KMO statistic (KMO = 0.905) and the result of the Bartlett's test (p < 0.001).> -wrap-foot>> -wrap-foot> VES-13 – Vulnerable Elders Survey.
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It uses six items — age, gender, total cholesterol, HDL cholesterol, smoking status, and systolic blood pressure — to calculate the odds of having a heart attack over the next 10 years.
Multivariate analysis identified three independent items: age, fracture type, and walking ability at discharge, as related to the status of anemia.
Demographic items (age, gender, education or race) and Computer Proficiency items were less influential.
21 items showed DIF with regard to the different levels of the variables "gender" (15 items), "age" (5 items), "pain" (1 item) and were therefore excluded.
Rates below 60% were shown for four results items (age, symptoms, quality of life, and specific adverse events).
The first section asked students to give personal details including the demographic items age and gender (summarised in Table 1).
Mean averages and confidence intervals for ratio scale items (age, experience, number of patients cared for on last shift) for the samples in each country were calculated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com